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Home All issues Volume 12 (2025) Manufacturing Rev., 12 (2025) 15 Full HTML
Open Access
Review
Issue
Manufacturing Rev.
Volume 12, 2025
Article Number 15
Number of page(s) 29
DOI https://doi.org/10.1051/mfreview/2025011
Published online 20 June 2025

© N. Lusi et al., Published by EDP Sciences 2025

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

The continued growth of the manufacturing sector, characterized by the utilization of advanced and highly competitive technology, necessitates machining and production techniques to achieve products of high-quality and precision. The current state of materials science has led to a increasing tendency to design and produce of intricate mechanical components. Consequently, there is a corresponding demand to integrate novel machining techniques and enhance existing ones within the manufacturing process. Advanced machining engineering processes are extensively employed as a means to address diverse challenges encountered in manufacturing operations. These challenges include the use of high-strength materials, fabrication of intricately profiled components, and demand for enhanced surface characteristics. Moreover, these methods are suitable for achieving high accuracy, miniaturisation, waste reduction, and process streamlining subsequent operations, all while permitting shorter production times. Due to its wide operational capabilities and high precision, abrasive waterjet (AWJ) machining has attracted significant interest from researchers and engineers in the manufacturing industry when compared to alternative machining techniques [1,2].

The commercialization of abrasive waterjet (AWJ) cutting began in the early 1970s, marking its initial commercial release. The first application of AWJ was developed in the 1970s with the aim of minimising production time and reducing thermal weaknesses commonly found in other non-conventional manufacturing techniques. The commercialisation of abrasive waterjet cutting was completed in the late 1980s [3]. AWJ machining initially faced limitations in the early 1980s. At its beginning, the technique was primarily applied on materials of lower hardness. However, due to technological advancements and the emergence of high-pressure waterjet generation, AWJ cutting is now capable of effectively cutting various materials while preserving their internal characteristics [4]. The utilisation of AWJ technology is presently observed in multiple industrial sectors, including automotive [5], electronic devices [6], geotechnical engineering [7], food manufacturing [8], marine applications [9], medical and surgical devicesf milling operation [10,11], and aerospace [12]. AWJ possesses a range of capabilities, enabling the utilisation of various AWJ operations such as milling [13–15], drilling [16–18], turning [19–21], threading and cleaning [22,23], peening [24–26], and hybrid machining [27].

The workpieces subjected to the AWJ process frequently display diverse shapes and dimensions, and possess the capacity to efficiently sever a broad spectrum of materials. The AWJ possesses the capability to perform cutting operations on both metal and non-metal materials. It exhibits proficiency in cutting various materials, including those that are soft and flexible such as glass [28] and plastic [29], as well as those that are considerably harder, such as titanium alloy [30–32], tool steel [33,34], and tungsten carbide [35]. The AWJ method is also employed for cutting advanced materials, including composites [36–38], ceramics [39,40], and other materials with magnetic properties.

Over the past three decades, many countries have undertaken extensive research on AWJ, resulting in the broadening of its application sector and scope. In recent years, the quantity of research articles in the AWJ field has significantly increased. Numerous researchers have undertaken early-stage investigations on AWJ and have derived numerous insightful findings. Emerging trends and themes are currently emerging. However, the existing body of study primarily emphasizes engineering applications. Nevertheless, there is a lack of analysis regarding the challenges encountered throughout research endeavors and prevailing trends in this field. Bibliometrics is a powerful technique and an effective tool for analyzing the general state of scientific research and examining the wealth of information contained in multiple publications. This approach leverages the interconnectedness of bibliographic networks to assess the scholarly merit and scientific influence of publications, as well as to forecast future trends in their growth. Furthermore, it is imperative to assess and critically assess the current literature to review research advances and identify possible avenues for future investigation. Hence, bibliometric analysis emerges as a valuable approach for assessing large-scale publication datasets due to its capacity to identify areas of knowledge deficiency, evaluate research quality, and identify recent advances within a specific domain.

This paper presents an extensive review of AWJ through the utilisation of bibliometric analysis indicators. This type of bibliometric analysis distinguishes itself from other types of literature reviews, such as thematic reviews, by employing objective quantitative and statistical methods, as well as utilising technological advancements like databases and software. Unlike thematic reviews, which rely on subjective manual processes and typically cover a limited number of articles (usually tens to hundreds), this bibliometric review offers a broader scope. Enabled by the use of advanced data analysis and machine learning techniques [41]. Bibliometric analysis indicators primarily attempt to quantitatively evaluate the quantity and significance of publications by taking into account the total number of scientific papers produced and the citations received. To assess the impact and influence of authors, journals, countries, and institutions in a particular research domain, bibliometric indicators are used, which play an important role. Moreover, bibliometric indicators can provide a comprehensive overview of a specific field of study. A number of scholars have presented bibliometric-based assessments in various disciplines with the aim of identifying the most dynamic and influential authors, publications, and institutions.

The purpose of this study is to classify and analyse recent publication trends in AWJ technology, determine the countries that have made the greatest contribution to this field, determine the most important journals and influential publications, and analyse keyword clusters. This study subsequently performed a systematic examination and evaluation of research publications that included case studies, with the objective of categorising their techniques. In addition to the general objectives, the specifics of this paper expand our understanding of the subject, provide context for application trends, suggest practical recommendations, and provide supporting references. In this way, gaps or opportunities for improvement in the unexplored field of AWJ can be identified.

2 Methodology

2.1 Bibliometric analysis

The utilisation of bibliometric analysis is a significant method for understanding and visually representing the accumulated scientific information and complex developments across established academic disciplines. This is accomplished by employing rigorous approaches to systematically analyse significant volumes of unstructured data [42]. The bibliometric review has been widely applied, covering an array of academic disciplines, including science and technology [43], social science [44], economics [45], education [46], business and management [47], health and medicine [48], and engineering [49]. Additionally, bibliometric analysis has been utilized in manufacturing research, including cloud manufacturing [50], additive manufacturing [51,52], sustainable manufacturing [53], lean manufacturing [54], and advanced manufacturing [55].

A bibliometric review involves a thorough assessment of designated databases, including Scopus [56], Web of Science [57], Google Scholar [58], Dimension [59], and PubMed [60], as well as additional literature sources that may not be accessible through these databases. A systematic method is required for the analysis and integration of relevant content in this type of evaluation. Software tools play a vital role in research as it aids researchers in identifying research challenges, displaying outcomes, analyzing data, and distributing knowledge. Regarding bibliometric analysis, numerous software packages for scientific atlases with varying capabilities and limitations have been developed. The information contained in publications can be extracted to the greatest extent by combining multiple software such as VOSviewer, Biblioshiny (tools inside the bibliometric package R), Histcite, CiteSpace, Gephi, and Leximancer. The presence of this software facilitates the analysis of data in a user-friendly and practical manner, hence leading to a recent increase in scholarly attention towards bibliometric analysis. The the software tools are open-source programs, provided at no cost and is designed with a user-friendly interface.

In bibliometric research, there are two primary methodologies for conducting bibliometric analysis. The initial step involves conducting a performance analysis, which assesses the productivity and impact of publications through the utilisation of diverse bibliometric indicators. These indicators encompass the number of publications, citations received, and average annual citations for publications that have attracted significant attention. Additionally, this analysis evaluates the influence of authors, nations, and journals within the scholarly community. Furthermore, the utilisation of scientific mapping analysis [61] encompasses components include clustering, visualisation of keyword co-occurrence analysis, bibliographic coupling, co-authorship analysis, citation analysis, and co-citation analysis. This comprehensive approach aids in mapping the knowledge structure in a particular area of study or journal [42].

2.2 Selection of bibliographic database

The dataset was compiled from the results obtained from subject search queries conducted on the Scopus core collection. The data covers the time period from 1980 to December 31, 2023. The aforementioned search query design is general enough that allows for its use to a science mapping investigation, unless, of course, the researcher possesses unrestricted access to the complete database. The dataset utilized in this investigation comprises articles and reviews that have been published in the English language. Researchers use the keywords “abrasive waterjet”, “abrasive water jet”, or “abrasive water-jet” within the “Title, Abstract, and Keywords” field on the Scopus search database to screen papers related to AWJ. However, irrelevant papers may have been inadvertently included in the data collection procedure. In order to reduce this circumstance, a manual screening process was conducted to exclude articles that lack relevance to AWJ. A comprehensive collection of 3022 documents was acquired, consisting primarily of articles (61%), conference papers (31%), book chapter (3%), conference review (2%) and a small percentage of other types of publications, as illustrated in Figure 1.

In the year 2023, the the dataset included 5,328 total subject classifications. Table 1 displays a thorough collection of the top nine study categories in the field of AWJ, as noticed in different scholarly journals. The field of engineering is the predominant category, with 2,312 articles. The subject areas include materials science (19.3%), computer science (9.0%), physics and astronomy (7.01%), chemical engineering (4.1%), earth and planetary sciences (3.2%), environmental science (2.9%), energy (2.6%), and mathematics (2.6%).

thumbnail Fig. 1

The document category obtained from the Scopus database.
Table 1

The top 9 groups for the subject areas of the study (data from Scopus database).

2.3 Inclusion criteria for literature selection

A total of 1,666 articles were identified in the previous methodological step (Tab. 2). Examining each article against a set of inclusion and exclusion criteria is essential for determining its suitability. The selection of these criteria aligns with the viewpoints of this review and is designed to be comprehensive to identify all relevant papers, while also clearly defining the research question. The research evaluated only peer-reviewed journal articles to ensure the quality of the selected sample, excluding other document categories such as books, chapters, conference proceedings, and editorial notes. During the screening procedure, irrelevant (i.e., publications that are outside the scope of this work) and non-English articles were eliminated. The dataset exhibits an annual growth rate of 12.67% over the full time span, including a mean of 18.74 references per document and a total of 34,958 references. Furthermore, the retrieved data unveiled an overall amount of 6,991 keywords and 3,062 author keywords that were categorized as either plus or index keywords from the 1,666 articles.

The investigation of the retrieved documents revealed that a total of 3,064 authors were engaged in performing research within this specific topic. Notably, single authors contributed to the authorship of 116 documents, indicating a significant level of individual productivity. On average, each article had 3.55 co-authors, suggesting a collaborative approach to research. Furthermore, foreign co-authorship accounted for 16.57% of the overall authorship, highlighting co-authorship contributed to the total international collaborations.

Table 2

The datasets collected.

2.4 Construction of citation and keyword networks

The chosen articles for the data collection procedure were imported into Biblioshiny and VOSviewer software in order to generate source-, country-, and author-level citation networks. This analysis aims to identify the authors and regions that have had the greatest impact on the field of AWJ research. Furthermore, keyword classification was also performed using the co-occurrence of keywords.

2.5 Visualisation of bibliometric data

Numerous software packages with diverse functionalities and limits were created for use in bibliometric analyses and science mapping. The integration of many software applications enables the comprehensive extraction of information from publications [62]. This study employed two distinct software bibliometric algorithms which is widely recognized as the most prominent software for conducting bibliometric analyses One notable package in R Studio is Biblioshiny, used to analyse the annual growth in publication frequency and changes in citation patterns. Furthermore, a collaborative technique was employed to perform a multi-country network analysis. This research focused on the co-citation of authors and the co-occurrence of keywords throughout various evaluation periods. The research was conducted utilising VOSviewer 1.6.18. The data given in Biblioshiny primarily relies on command-line execution, unlike VOSviewer, which provides a graphical interface.

2.6 Analytical procedure

To gain deeper insight into AWJ technology, a two-phase methodology was applied [63]. The research investigation centered on two distinct categories of bibliometric indicators.  The first category included conventional bibliographic information, encompassing details such as affiliations, authors, publication year, and sources (e.g. journal titles). The second group comprised terms obtained from the abstracts and titles of research articles using machine learning methods. The R software tool was used in both phases of the investigation. The second phase involves the identification of clusters, which are crucial in deciding the articles that will be incorporated for inclusion in the review section. The purpose of this investigation was to obtain social insight, intellectual framework, and conceptual of the subject's study. The review process followed in this work is depicted in Figure 2.

thumbnail Fig. 2

A bibliometric review's methodology.

3 Results

3.1 Trends in annual publication volume

This study examines the research on AWJ by analyzing the documents generated on the subject across a span of four decades, beginning in 1980 with one article by Griffiths and Godding [64]. The frequency of published articles has demonstrated a gradual increase from 1980 to 2023, as depicted in Figure 3. Between the years 1980 and 2008, there was a marked lack of growth in scientific publications, resulting in a tendency towards a state of stagnation. This was particularly evident in the fewer than fifty publications per year. The quantity of published articles shown a substantial growth over the years 2015 and 2022, and has steadily risen during the preceding five-year period. The annual growth rate in publications corresponds to 12.67%. Furthermore, in recent years, there has been a notable rise in the quantity of citations for publications, indicating a heightened level of scholarly interest in the subject matter.

The year 1993 had a notable surge in research output, with a growth rate of 228.57% compared to the preceding year. A comparable pattern is also observed in the proportion of publications as the years ago. Four distinct characteristics were observed in the AWJ research articles, as indicated in Table 3. During the initial decade spanning from 1980 to 1990, a 87 papers were analysed, which represented a consistent pattern. Following that, a total of 167 articles (indicative of a consistent pattern) were recorded between 1991 and 2000, whereas 256 articles were recorded between 2001 and 2010. Subsequently, a additional 1,156 documents were examined from 2011 to 2023. The final characteristic of the research output demonstrated a significant increase in publications, a trend that is expected to persist as the recognition of the advantages associated with the utilisation of AWJ becomes more widespread. Consequently, this is likely to stimulate research interest and foster further advancements in various fields of study. Figure 4 illustrates the mean number of citations accumulated annually. The phenomenon of citations is currently experiencing a growing trend, although the growth rate is non-uniform.

thumbnail Fig. 3

Annual scientific production.
Table 3

Research publication growth.

thumbnail Fig. 4

The average yearly amount of acquired citations.

3.2 Leading journals in AWJ research

A comprehensive review leading journal titles is presented in Table 4. This includes the number of publications, citations, CiteScore, total link strength, impact factors (IF), h-index, and quartile rankings associated with each title. The aforementioned data was acquired through the utilisation of the VOS viewer software, which performs citation analyses on sources. The IJAMT achieved the most prominent ranking among the top 20 journals, accumulating a total citation count of 4,038. The journal is well regarded for its significant impact factor of 3.4, a cite score of 6.2, an H-index of 145, and a Q1 ranking. It is widely recognized as the leading journal in the field of abrasive waterjet research, offering a comprehensive collection of scientific articles. Its reputation extends globally, solidifying its position as the foremost publication in this discipline. The observed result can be attributed to the alignment between the journal's specific emphasis on manufacturing and the primary research focus on abrasive waterjet technology across various applications within the field of AWJ study.

The study of scientific research published in the leading 20 journals reveals a notable emphasis on sophisticated machining and manufacturing, alongside the use of machining technologies. The data aligns with the scope of AWJ research, which focuses on the use of AWJ, as a highly advanced machining technique derived from the field of manufacturing technology. The findings presented in Table 4 indicate that a significant proportion of the top 20 articles (fourteen out of twenty) place a high emphasis on scientific inquiries pertaining to machining processes. The aforementioned findings provide support for the conclusions drawn regarding the primary subject areas in which engineering achieved the greatest rankings. The data may be attributed to the fact that engineering represents the tangible application of scientific findings. Based on the given criteria, which include the impact factor (IF) range of 1.2–14, the Cite Score range of 2.4–20.9, and the observation that over 50% of the journals possess a Scimago® grade of Q1, it can be inferred that the 20 highest-ranked journals have exceptional quality and significant scientific effect.

The evaluation of the quality of academic journals can be accomplished by using the impact factor, as demonstrated in Table 4. Based on an analysis of publication counts and citation metrics across various academic disciplines, it has been observed that the IJAMT and the JMPT have recorded high publication and citation counts. The IJMTM achieved the greatest impact factor among the 20 journals. Furthermore, it is worth mentioning that the JMPT has recorded the highest h-index (215) in comparison to other prominent and prolific journals across many fields of publication.

Table 4

The top 20 highly cited journals within the subject of AWJ research.

3.3 Leading authors in AWJ research

The assessment of an author's productivity can be conducted by the total number of published papers they have generated. Similarly, the evaluation of the impact of their research can be performed by calculating the total number of citations received by their published works. The h-index, also known as the Hirsch index [65], is a composite metric that considers both productivity and effect. The h-index is a metric used to evaluate the scholarly productivity of academics based on the quantity of their publications and the extent to which these publications have been cited [66]. The g-index is a metric that has been developed to assess the overall citation impact of a collection of scholarly papers. The g-index is a metric that reflects high-impact contributions [67]. In order to calculate an individual's m-index, the h-index is divided by the number of years since their first publication. Table 5 shows the twenty authors who have received the greatest number of citations. Wang J was identified as the author who has authored the most publications and received the most citations within a group of 20 authors. Specifically, Wang J received 2,731 citations from 64 articles, with the initial article dating back to 1999. Hashish M ranked second in total citations, with 1,843 citations received from 51 publications. These articles span from the first publication in 1982. Furthermore, it is worth noting that Wang J and Hashish M have achieved the highest h-index scores of 28 and 21, respectively.

Table 5

The author's local Impact in AWJ research.

3.4 Most cited articles in AWJ research

The classification of influential papers in the subject of abrasive waterjet has been carried out using articles that have the highest citation counts. The work that possesses a greater number of citations is considered more influential owing to the presence of novel and valuable ideas incorporated within its contents. Table 6 displays the 20 most frequently most-cited publications in AWJ over the full time span. The articles that received the greatest amounts of citations was authored by Hashish M in 1984.

Table 6

lists the most-cited articles.

3.5 Country-level publication output and collaborations

Among the 80 countries analysed in Figure 5, India is the leading contributor with 381 articles, accounting for approximately 22.85% of total publications in this study area. China ranks second with 314 items, representing 18.88% of the total output, while the United States comes third with 199 items, representing 11.96%. Figure 6 displays the worldwide production of publications in AWJ distribution. The country collaboration structure demonstrates a plenty of cross-continent collaborative relationships in general (Fig. 7). The global network of scientific partnerships is highlighted in the country collaboration map for AWJ research, with the United States, China, and India contributing the most, as shown by the connecting lines and darker shades. The Asia-Pacific region leads in research output, which includes China, India, and Australia. Australia maintains links with Asian and European countries. European nations also exhibit active research partnerships, especially Germany, the UK, and Italy. Cross-border partnerships are indicated by the numerous connections between Europe and North America (USA, Canada). Several countries appear to have no recorded collaboration in AWJ research, including most African nations, selected South American nations (e.g. Bolivia, Paraguay), and Central Asian countries like Kazakhstan and Uzbekistan. Additionally, some Middle Eastern countries and a few Eastern European nations show limited or no research connections. This indicates that AWJ research is concentrated in specific regions, with limited participation from developing or less industrialised regions.

thumbnail Fig. 5

The 20 most productive countries of AWJ research.
thumbnail Fig. 6

The Global perspective on the research productivity of nations from 1980 to 2023.
thumbnail Fig. 7

The international collaboration's map between countries.

3.6 Network keyword analysis

Co-occurrence refers to the occurrence of certain items simultaneously, such as author keywords, index of words, or entity features, within an article. It is a quantifiable analysis of keyword relationships to uncover the implicit significance of evidence and concealed information linked to a specific object. Keywords help define, map, and trace both popular and emerging fields of study. The minimal threshold for the occurrence of a keyword was set at 10 instances. A total of 85 keywords, out of a pool of 3,023 keywords, met the occurrence threshold. The node's size in the visualisation map is proportional to the keyword's contribution, with larger nodes representing higher occurrences of the keyword.

The characterisation of the eight groupings of cooccurrences is illustrated in Figure 8. They were divided into the following clusters: Cluster 1 consisted of 19 elements in red, Cluster 2 consisted of 18 elements in green, Cluster 3 consisted of 16 elements in blue, Cluster 4 consisted of 10 elements in yellow, Cluster 5 consisted of 10 elements in purple, cluster 6 of 6 elements in cyan, cluster 7 of 5 elements in orange and cluster 8 of 1 element in dusty pink. As defined in the first cluster, “Abrasive waterjet cutting and cutting performance on titanium “, once the most frequent terms refer to kerf characteristics, surface integrity, erosion, fatigue and milling. The second cluster is defined as “Artificial neural network (ANN) and regression analysis in AWJ machining”. This cluster encompasses the machining performance within AWJ, including surface roughness, surface morphology, material removal rate, machinability and mechanical properties. The third cluster is defined as “Computer fluid dynamic (CFD) and numerical simulation”. Once the main occurrences are related to wear, surface quality, vibration, delamination, and declination angle. The fourth cluster is defined as “Optimisation of AWJ”, once the main occurrences are related to the Taguchi method, ANOVA, kerf width, and cutting depth. The fifth cluster is defined as “Abrasive and composite materials”, once the main occurrences are related to kerf, taper, surface, and roughness”. Cluster six is defined as “Garnet and Abrasives”, once the main occurrences are stand of distances (SOD), taper angle and traverse speed”. The seven cluster is defined as “ CFRP (carbon fiber-reinforced plastic)”, once the most frequent term is composite materials. The eight cluster is defined as “SEM (Scanning electron microscope)”.

This section also provides a comprehensive analysis and evaluation of index keyword frequency in AWJ of Scopus datasets from 1980 to 2023. The selection of index keywords for AWJ is based on the co-occurrence of specific index keywords. The AWJ dataset consists of 7002 index terms, out of which 136 satisfy the condition of co-occurring at least 20 times. The collection of interconnected things is remarkably vast, consisting of 136 index phrases. These terms are meticulously organized into four clusters, and with a combined link strength of 30183. Figure 9 presents the co-occurrence network diagram of the co-occurrences among index-keywords. The term “Jets” has a total link strength of 5,726 and occurred 910 times. The term “abrasive” has a combined link strength of 3654 and appears 545 times. Lastly, the term “surface roughness” has a combined link strength of 2754 and appears 364 times.

thumbnail Fig. 8

Network diagram of author keywords (threshold: 10 co-occurrence) and normalisation method (Association strength).
thumbnail Fig. 9

Network diagram for index keywords (threshold: 20 co-occurrence).

3.7 Word cloud

As a representation of words, a word cloud displays the frequency of occurrence of each word, with its size corresponding to that frequency. Because they are larger and more noticeable, important words are usually positioned in the center of the word cloud, which is often arranged randomly. Figure 10 shows a diagram of the words that appeared the most frequently in AWJ-related articles. The word cloud analysis was conducted over four time periods to examine the evolution of keywords in AWJ research. The magnitude of a word is determined by the number of times it appears in the word cloud. The word placement is random, however the most important keywords are in the center to bring attention to their large size. “Metal cutting” and “jet cutting” are the most researched subject in the first period (1980-1990). “Mathematical model”, “erosion”, “wear of materials” and “nozzles”, “ceramic materials” are the keyword that mainly used in the second period (1991-2001). During the third period (2002-2012), the research topic remained mostly unchanged in comparison to the previous period. This is evidenced by the consistent use of several keywords that were also present in the previous period. “Standoff distance” (SOD), “surface roughness” and “scanning electron microscope” are keywords that appear a lot in the recent decade (2013-2023). The term “nozzle” regularly co-occurs with “erosion” in every time period, and there is a decline in keyword usage of this keyword, as indicated by the reduced word size in the visualisation in the word cloud. Simultaneously, it is evident that no singular primary research tendency prevails in frequency over others. Each term in the word cloud chart can be interpreted as key themes of development of development and study carried out in the field of AWJ [88].

thumbnail Fig. 10

Word cloud of keyword plus.

3.8 Bibliographic coupling of research articles

Bibliographic coupling is a scientometric technique allows researchers to look at a variety of subjects, spot new trends, and think about where to take their research in a given field. Citations to a shared third source in two publications imply that the authors of those publications have similar intellectual underpinnings and build upon them [89]. Bibliographic coupling can identify thematic or social clusters, which allow researchers to monitor the development of a theme. This method aids in understanding how research fields advance, interconnect and shape each other over time. This study used a bibliographic coupling technique to identify current research trends in the scientific literature on abrasive waterjet and to provide a relevant research focus for future work. A citation threshold of 25 per article was set for curationwhich resulted in the selection of 372 research papers that linked together. The final cluster groupings and number of associated documents are shown in Figure 11 and the elements of each cluster are summarised in Table 7. Each node represents a research article, with its size indicating the number of citations received. The connections between nodes reflect shared references, illustrating intellectual relationships and thematic clusters within the research field. Colours denote distinct research communities or topic areas, revealing key contributors and influential works in the domain. The proximity of nodes indicates a stronger thematic connection between studies, helping to identify research trends and the evolution of knowledge within the field.

thumbnail Fig. 11

Bibliographic coupling of articles.
Table 7

List of documents Co-citation articles in the clusters (1980-2023).

3.9 Thematic assessment of the clusters

The basic theories of AWJ machining and tool wear are the subject of Cluster 1 (red). This cluster is the largest with 93 articles, demonstrating its fundamental significance in AWJ research. The representative articles most likely examine early and crucial studies that have influenced our knowledge of erosion models [79], material removal procedures [95], and AWJ cutting mechanisms. The erosion mechanics involved in AWJ cutting have been the main focus of studies in this cluster. This field relies heavily on theories like plastic deformation and brittle fracture mechanisms under high-pressure water [82] and abrasive impact.

AWJ can be contrasted with other cutting techniques in studies (e.g. laser or EDM) in terms of precision and durability. Tool wear and the effectiveness of material removal are greatly influenced by the size, shape, and hardness of the abrasive particles. degradation is a major area of research because AWJ depends on high-velocity abrasive streams. Research investigates the long-term effects of recycling and abrasive fragmentation techniques on jet performance. Research on wear-resistant materialssuch as composite-based nozzles, advanced ceramics, and tungsten carbide has emerged as a critical area in the development of AWJ technology. Enhancing the operational efficiency of AWJ systems necessitates a comprehensive understanding of the interplay between impact angle, material hardness, and jet pressure. Recent advancements in multi-phase flow modeling have contributed significantly to optimising jet velocity and the transfer of impact energy, thereby minimising tool wear and extending the service life of AWJ components.

A notable change in AWJ research toward computational modeling and simulation-based methodologies is Cluster 2 (green) addresses. This pattern demonstrates the growing use of machine learning (ML), artificial intelligence (AI), and numerical simulations to improve cutting efficiency, forecast material removal rates, and optimize AWJ parameters. In order to comprehend jet behaviour, pressure distribution, and particle interactions, early research in this cluster concentrated on finite element analysis (FEA), computational fluid dynamics (CFD), and empirical modeling. Improved control over kerf width, surface roughness, and energy efficiency has resulted from researchers' ability to examine intricate interactions between high-speed abrasive particles and target materials thanks to developments in multi-physics simulations. In order to adjust AWJ parameters in real-time, recent studies show increasing integration of deep learning, neural networks, and evolutionary algorithms (such as particle swarm optimisation and genetic algorithms). Furthermore, new technologies that improve accuracy and sustainability include digital twins and real-time process monitoring with IoT and smart sensors.

The growing emphasis on multi-material studies and process development in abrasive waterjet technology Cluster 3 (blue) focuses on. The need to optimise cutting and machining of a variety of materials, such as metals, composites, ceramics, polymers, and hybrid materials, is highlighted by this trend. Understanding how various materials respond to high-pressure water jets has become a crucial area of research as sectors like aerospace, biomedical, and automotive increasingly use waterjet technology for complex material processing. Early research in this cluster mostly concentrated on the cutting performance and material removal mechanisms of specific materials, paying particular attention to variables like thickness, pressure, cutting speed, and distance. Research on multi-material cutting has grown over time. In this process, AWJ interacts with layered, bonded, or other materials, posing issues with kerf taper, uneven erosion, and delamination. In order to improve surface integrity and accuracy, recent advancements emphasize the optimisation of AWJ in composite and functionally graded materials, including hybrid cutting techniques. The capacity of AWJ to create complex materials with little thermal degradation has been further enhanced by developments in simulation tools, real-time monitoring, and artificial intelligence-driven optimisation.

Cluster 4 (yellow) explores recent advancements in optimisation and modeling for AWJ machining, reflecting a significant shift toward data-driven decision-making and predictive analytics in manufacturing. As AWJ technology becomes more integrated into precision manufacturing industries, researchers have emphasized the need for optimized process parameters to achieve higher cutting efficiency, improved surface quality, and reduced material wastage. Initially, AWJ optimisation relied on trial-and-error experiments to determine the best cutting parameters, such as water pressure, traverse speed, abrasive flow rate, and stand-off distance. However, due to the complex, non-linear nature of AWJ processes, traditional methods often led to inefficiencies and high operational costs. To address this, computational optimisation techniques were introduced, leading to multi-objective optimisation approaches that consider trade-offs between cutting speed, surface roughness, kerf taper, and material removal rate. The introduction of techniques like response surface methodology (RSM), Taguchi methods, and grey relational analysis (GRA) improved AWJ performance by systematically optimising process parameters.

One of the most significant recent trends in this cluster is the integration of artificial intelligence (AI) and machine learning (ML) into AWJ optimisation. AI-driven models provide a more adaptive and predictive approach to optimising AWJ parameters, minimising defects, and improving process stability. Neural networks and deep learning algorithms have been developed to predict surface roughness, kerf width, and cutting efficiency based on vast datasets. Genetic algorithms (GA) and particle swarm optimisation (PSO) have been applied to find optimal parameter combinations with limited experimental trials. Fuzzy logic and reinforcement learning are being explored to create adaptive control systems that dynamically adjust parameters during cutting operations. The future of AWJ optimisation will likely be driven by autonomous systems, real-time adaptive controls, and hybrid digital twin approaches, transforming AWJ into a more intelligent and self-optimising machining technology.

Material interaction and erosion mechanism in AWJ machining is the Cluster 5 (purple) covers, a crucial field that has a direct impact on cutting performance, material integrity, and process efficiency. This cluster investigates the underlying wear mechanisms, how different materials react to fast-moving abrasive particles, and methods for maximising material removal while reducing surface damage. The impact dynamics of abrasive particles largely control material erosion in AWJ cutting. brittle fracture mode: In ceramic, glass, and brittle composites, material removal happens as a result of crack propagation brought on by impacts from high-energy particles. This is one of the main mechanisms examined in this cluster. Chipping and uneven surface morphology are frequently the results of this. Ductile cutting mechanism: Plastic deformation, plowing, and micro-cutting are methods used to remove material from metals and polymers. This produces a smoother finish but may also leave residual stress and heat-affected areas. Mixed-mode erosion: Because composite materials react in both brittle and ductile ways, it can be difficult to achieve consistent surface quality.

Cluster 6 explores AWJ applications in rock and granite machining, particularly for industries such as construction, mining, monument engraving, and decorative stone fabrication. The increasing demand for precision cutting, minimal material wastage, and environmentally friendly machining methods has driven the adoption of AWJ as a superior alternative to traditional diamond saws and mechanical cutting tools. Due to the high hardness and abrasive nature of granite and similar stones, researchers in Cluster 6 are focusing on optimising process parameters to improve cutting efficiency, precision, and tool longevity.

Cluster 7 addresses secondary jet effects and defect control in AWJ. One of the key challenges in AWJ is the uncontrolled secondary jet formation, which can cause surface defects, material delamination, excessive erosion, and geometrical inaccuracies. This cluster explores research efforts aimed at understanding, predicting, and minimising the effects of secondary erosion to enhance the precision and quality of AWJ-processed components. Cluster 7 highlights the challenges associated with secondary jet erosion in AWJ and the research efforts to mitigate its impact. By combining experimental studies, numerical simulations, and hybrid machining approaches, researchers aim to minimize surface defects and enhance machining precision.

Cluster 8 investigates hybrid AWJ techniques, integrating AWJ with complementary technologies to enhance machining performance, expand material compatibility, and improve precision. Traditional AWJ machining is widely recognized for its cold-cutting nature, minimal heat-affected zones, and high versatility. However, limitations such as poor surface finish, excessive taper, and reduced efficiency in hard or composite materials have led to the development of hybrid AWJ techniques.

This cluster explores advanced AWJ modifications and hybridisation strategies, including laser-assisted AWJ (LAWJ), cryogenic AWJ (CAWJ), ultrasonic AWJ (UAWJ), and electrochemical AWJ (ECAWJ). These enhancements enable AWJ to be used in precision manufacturing, high-strength material processing, and micro-machining applications. Cluster 8 highlights the emerging trends in hybrid and advanced AWJ applications, showcasing how technological integrations can expand AWJ capabilities beyond traditional machining. Innovations such as laser, cryogenic, ultrasonic, and electrochemical-assisted AWJ have significantly improved efficiency, surface quality, and material adaptability.

Cluster 9 analyses erosion and corrosion in laser-cladded surfaces, highlighting the interplay between surface modifications, wear resistance, and material longevity in harsh environments. Laser cladding (LC) is widely used to enhance surface properties of metals and alloys, particularly in aerospace, marine, oil & gas, and manufacturing industries. However, despite its benefits, laser-cladded coatings are still susceptible to erosion and corrosion, especially in high-speed fluid environments, abrasive conditions, and chemically aggressive atmospheres.

Cluster 10 examines multi-physics and multi-scale simulations in AWJ research, aiming to enhance the fundamental understanding of AWJ processes by integrating fluid dynamics, particle mechanics, material deformation, and surface interactions. These studies contribute to improving the precision, efficiency, and adaptability of AWJ cutting, machining, and surface treatment applications.

4 Discussion

4.1 Future research path in AWJ

This review presents a comprehensive overview of AWJ technology, with an emphasis on challenges and recent advances in AWJ research. A thematic classification of article divisions was developed using the results obtained from the citation analyses conducted in Biblioshiny and VOSviewer. This was accomplished although a comprehensive analysis of the publication's content is not within the scope of this review. The outcomes of this analysis are consistent with those of the theme analyses that employ word clouds and keyword analyses. There are still numerous unresolved discussions and unresolved research inquiries that present opportunities for a deeper comprehension of particular ideas or concepts (Table 8). These inquiries may be addressed through future investigations.

Table 8

Proposed areas for future investigation cited in publications with significant influence.

4.2 Implications and limitation

Key bibliometric indicators characterising AWJ literature have also been explicitly defined and presented. The obtained bibliometric indicators, both quantitative and qualitative, will support future performance evaluations. These can serve as a means of evaluating advancements in terms of collaboration, citation impact, and research output, and other aspects against the present situation. This evaluation offers an comprehensive set of bibliometric insights that allows researchers to learn about the contributions made by authors, organisations, and nations in the area of expert system research. This tool can be employed to assess progress concerning citations, productivity, collaboration and other relevant factors, in comparison to the current state. Moreover, it is feasible to utilise some indicators to identify potential advancements in research trends and interests in comparison to current trends. The research has a few drawbacks that can guide future studies, considering the study's scope and the review approach employed. Due to the dataset's time range spanning from 1980 to 2023, there is a potential omission of recent or in-press publications. This review presented a number of important details that interested researchers can use to learn more about the contributions by authors, institutions, and countries in expert system research disciplines. Furthermore, some of these indicators can be used to find possible advancements in the interests and trends of research. when contrasted to current patterns. Citation rates represent the biggest restriction. There may be variations in the citation rates acquired from different databases compared to the Scopus database (e.g., WOS, Google Scholar, etc.). Despite this disadvantage, Scopus is commonly used for bibliometric evaluations to perform excellent bibliometric analyses because it has several advantages and is more flexible than other databases. Another disadvantage is that our research only looked at publications and review materials. The absence of other sorts of documents (such as letters, conference papers, editorials, and so on) may result in the loss of vital information and contributions to the area, despite the fact that their inclusion might have enhanced its credibility. studies excluding local-language content will experience adverse consequences. Further investigation ns may aim to broaden the scope of the findings through the inclusion of a more extensive variety of phrases, acknowledging limitations due to keyword-based retrieval.

5 Conclusion

The evolution and advancement of AWJ technology are comprehensively investigated in this study, which presents a bibliometric analysis over a four-decade period from 1980 to 2023. A total of 1666 publications indexed in the Scopus database between 1983 and 2023 were systematically reviewed and analysed using VOSviewer and Biblioshiny (RStudio) to identify key trends and research directions in the AWJ field. Specifically, research in the field of AWJ technology has demonstrated rapid growth, evidenced by a significant publication increase during 1993, showing a 228.57% growth. The strong presence of engineering disciplines is evident, accounting for 43.40% of the total publications, which underscores the sustained and substantial interest of the engineering research community in advancing AWJ processes and applications. This study identifies key milestones and research shifts within AWJ-related research, offering a quantitative performance assessment of influential sources, authors, highly cited articles, and contributing countries. The findings aim to provide a consolidated understanding of the current research landscape and inform future directions in the domain of AWJ technology within manufacturing science. In summary, this review provides valuable insights into AWJ technology by systematically identifying and analyzing key terminology within the existing literature, mapping out thematic knowledge clusters and semantically aligned research communities. It further synthesizes previous studies and proposes future research pathways to guide future investigations in the field. This comprehensive review highlights several significant developments in AWJ technology:

  • Studies were categorised using bibliographic coupling and categorized into the ten previously identified research themes: fundamental theories of AWJ and tool wear, computational models and simulation-based approaches, multi-material studies and process development, recent advancements in optimisation and modelling, material interaction and erosion mechanism, industrial use cases and large-scale manufacturing, secondary jet erosion and defect mitigation, hybrid and advanced AWJ applications, erosion and corrosion of laser cladding, and multi-physics and multi-scale modelling.

  • Analysis of co-occurring keywords indicates that surface integrity, erosion, and fatigue are among the most frequently addressed topics in current AWJ research. Additionally, the increasing use of artificial intelligence techniques such as artificial neural networks (ANN), regression analysis, and other machine learning methods reflects a growing interest in predictive modeling of AWJ performance characteristics. The future of AWJ optimisation is expected to evolve through the integration of AI, autonomous systems, real-time adaptive control, and hybrid digital twin technologies, enabling smarter and more self-optimising machining processes.

  • Researchers will have numerous opportunities in the future to explore cost-effective and sustainable abrasives, reconditioning nozzles to minimise waste, and combining additive manufacturing and AWJ to develop hybrid manufacturing frameworks for industrial use.

The AWJ serves as a principal hub for frontier research in the fields of manufacturing, specializing in advanced machining processes that enable high precision, minimal thermal distortion, and the ability to cut complex materials. It functions as a nurturing environment for noteworthy the newest topics in AWJ research as well as developing trends. In summary, this bibliometric assessment is dynamic and subject to future refinement. Therefore, it is essential to conduct these analyses regularly to capture achievement and shortcomings in relation to the various bibliometric performance metrics of a particular journal.

Acknowledgments

The authors gratefully acknowledge the technical and financial support of Universiti Malaysia Pahang Al-Sultan Abdullah.

Funding

This research was funded by Universiti Malaysia Pahang Al-Sultan Abdullah through PGRS 230327, the Ministry of Higher Education Malaysia through PRGS/1/2024/TK10/UMP/02/1 (RDU240802) and Vice Chancellor Scholarship (VCS).

Conflicts of interest

The authors declare no conflict of interest.

Data availability statement

This article has no associated data generated.

Author contribution statement

Nuraini Lusi: conceptualisation, literature review, methodology, resources, writing—original draft preparation, analysis, visualisation and validation. IGNB Catrawedarma: resources, analysis, visualisation and validation. Mebrahitom Gebremariam: review the data, analysis. Kushendarsyah Saptaji: review citation, analysis. Azmir Azhari: conceptualisation, review, writing—original draft preparation, validation, funding acquisition, resources, supervision.

References

  1. A. Anu Kuttan, R. Rajesh, M. Dev Anand, Abrasive water jet machining techniques and parameters: a state of the art, open issue challenges and research directions, J. Brazilian Soc. Mech. Sci. Eng. 43 (2021) 1–14 [CrossRef] [Google Scholar]
  2. Y. Natarajan, P.K. Murugesan, M. Mohan, S.A. Liyakath Ali Khan, Abrasive Water Jet Machining process: A state of art of review, J. Manuf. Process. 49 (2020) 271–322 [CrossRef] [Google Scholar]
  3. A.N. Raipur, A review paper on current research and development in abrasive waterjet machining, Int. J. Innov. Sci. Res. Technol. 71 (2022) 4160–4169 [Google Scholar]
  4. H. Singh, N.K. Bhoi, P.K. Jain, Developments in abrasive water jet machining process − from 1980 to 2020, in: Adv. Mach. Finish., Elsevier, 2021, pp. 217–252 [CrossRef] [Google Scholar]
  5. M. Schüler, D. Heidrich, T. Herrig, X.F. Fang, T. Bergs, Automotive hybrid design production and effective end machining by novel abrasive waterjet technique, Procedia CIRP 101 (2021) 374–377 [CrossRef] [Google Scholar]
  6. V.K. Pal, S.K. Choudhury, Fabrication and analysis of micro-pillars by abrasive water jet machining, Procedia Mater. Sci. 6 (2014) 61–71 [CrossRef] [Google Scholar]
  7. H. Li, Z. Huang, J. Li, K. Cheng, J. Hu, W. Li, Effect of nozzle structure on rock drilling performances of abrasive waterjet, in: ARMA US Rock Mech. Symp., ARMA, 2023, p. ARMA–2023 [Google Scholar]
  8. J. Valentinčič, A. Lebar, I. Sabotin, P. Drešar, M. Jerman, Development of ice abrasive waterjet cutting technology, J. Achiev. Mater. Manuf. Eng. 81 (2017) 76–84 [Google Scholar]
  9. M.K. Singh, R. Trehan, A. Gupta, Application of Grey approach to enhance the surface properties during AWJ machining of marine grade Inconel, Adv. Mater. Process. Technol. 7 (2021) 429–445 [Google Scholar]
  10. G. Li, S. Ding, Machining of medical device components, in: Met. Biomater. Process. Med. Device Manuf., Elsevier, 2020, pp. 137–157. [Google Scholar]
  11. M.C. Kong, D. Axinte, W. Voice, Challenges in using waterjet machining of NiTi shape memory alloys: An analysis of controlled-depth milling, J. Mater. Process. Technol. 211 (2011) 959–971 [CrossRef] [Google Scholar]
  12. S. Akıncıoğlu, Investigation of effect of abrasive water jet (AWJ) machining parameters on aramid fiber-reinforced polymer (AFRP) composite materials, Aircr. Eng. Aerosp. Technol. 93 (2021) 615–628 [CrossRef] [Google Scholar]
  13. N. Yuvaraj, E. Pavithra, C.S. Shamli, Investigation of surface morphology and topography features on abrasive water jet milled surface pattern of SS 304, J. Test. Eval. 48 (2020) 2981–2997 [CrossRef] [Google Scholar]
  14. Y. Yuan, J. Chen, H. Gao, X. Wang, An investigation into the abrasive waterjet milling circular pocket on titanium alloy, Int. J. Adv. Manuf. Technol. 107 (2020) 4503–4515 [CrossRef] [Google Scholar]
  15. V.K. Pal, A.K. Sharma, Complex shaped micro-channels generation using tools fabricated by AWJ milling process, Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 236 (2022) 194–201 [CrossRef] [Google Scholar]
  16. M. Altin Karataş, A.R. Motorcu, H. Gökkaya, Study on delamination factor and surface roughness in abrasive water jet drilling of carbon fiber-reinforced polymer composites with different fiber orientation angles, J. Brazilian Soc. Mech. Sci. Eng. 43 (2021) 1–29 [CrossRef] [Google Scholar]
  17. S. Mahalingam, B. Kuppusamy, Y. Natarajan, Multi-objective soft computing approaches to evaluate the performance of abrasive water jet drilling parameters on die steel, Arab. J. Sci. Eng. 46 (2021) 7893–7907 [CrossRef] [Google Scholar]
  18. Y. Zhang, D. Liu, W. Zhang, H. Zhu, C. Huang, Damage study of fiber-reinforced composites drilled by abrasive waterjet—challenges and opportunities, Int. J. Adv. Manuf. Technol. (2021) 1–15 [Google Scholar]
  19. D. Liu, C. Huang, J. Wang, H. Zhu, Material removal mechanisms of ceramics turned by abrasive waterjet (AWJ) using a novel approach, Ceram. Int. 47 (2021) 15165–15172 [CrossRef] [Google Scholar]
  20. A.K. Srivastava, A. Nag, A.R. Dixit, J. Scucka, S. Hloch, D. Klichová, P. Hlaváček, S. Tiwari, Hardness measurement of surfaces on hybrid metal matrix composite created by turning using an abrasive water jet and WED, Measurement 131 (2019) 628–639 [CrossRef] [Google Scholar]
  21. F. Kartal, A. Kaptan, Influence of abrasive water jet turning operating parameters on surface roughness of ABS and PLA 3D printed parts, Int. J. 3D Print. Technol. Digit. Ind. 7 (2022) 184–190 [Google Scholar]
  22. L. Wan, J. Xiong, J. Cai, S. Wu, Y. Kang, D. Li, Feasible study on the sustainable and clean application of steel slag for abrasive waterjet machining, J. Clean. Prod. 420 (2023) 138378 [CrossRef] [Google Scholar]
  23. L. Huang, P. Kinnell, P.H. Shipway, Parametric effects on grit embedment and surface morphology in an innovative hybrid waterjet cleaning process for alpha case removal from titanium alloys, Procedia CIRP 6 (2013) 594–599 [CrossRef] [Google Scholar]
  24. A. Skoczylas, Vibratory shot peening of elements cut with abrasive water jet, Adv. Sci. Technol. Res. J. 16 (2022) [Google Scholar]
  25. V. Chakkravarthy, J.P. Oliveira, A. Mahomed, N. Yu, P. Manojkumar, M. Lakshmanan, L. Zhang, V. Raja, S. Jerome, T.R. Prabhu, Effect of abrasive water jet peening on NaCl-induced hot corrosion behaviour of Ti-6Al-4V, Vacuum 210 (2023) 111872 [CrossRef] [Google Scholar]
  26. Z. Lv, R. Hou, R. Wang, Y. Zhang, M. Zhang, Investigation on surface integrity and fatigue performance in abrasive waterjet peening, J. Brazilian Soc. Mech. Sci. Eng. 44 (2022) 520 [CrossRef] [Google Scholar]
  27. X. Yang, X. Lin, M. Li, X. Jiang, Experimental study on surface integrity and kerf characteristics during abrasive waterjet and hybrid machining of CFRP laminates, Int. J. Precis. Eng. Manuf. 21 (2020) 2209–2221 [CrossRef] [Google Scholar]
  28. M. ShivajiRao, S. Satyanarayana, Abrasive water jet drilling of float glass and characterisation of hole profile, Glas. Struct. Eng. 5 (2020) 155–169 [CrossRef] [Google Scholar]
  29. R. Shanmugam, M. Thangaraj, M. Ramoni, Enhancing the performance measures of abrasive water jet machining on drilling acrylic glass material, in: ASME Int. Mech. Eng. Congr. Expo., American Society of Mechanical Engineers, 2022, p. V02AT02A044 [Google Scholar]
  30. Y. Yuan, J. Chen, H. Gao, Surface profile evolution model for titanium alloy machined using abrasive waterjet, Int. J. Mech. Sci. 240 (2023) 107911 [CrossRef] [Google Scholar]
  31. X. Sourd, R. Zitoune, A. Hejjaji, M. Salem, A. Hor, D. Lamouche, Plain water jet cleaning of titanium alloy after abrasive water jet milling: Surface contamination and quality analysis in the context of maintenance, Wear 477 (2021) 203833 [CrossRef] [Google Scholar]
  32. P. Karmiris-Obratański, N.E. Karkalos, R. Kudelski, E.L. Papazoglou, A.P. Markopoulos, Experimental study on the correlation of cutting head vibrations and kerf characteristics during abrasive waterjet cutting of titanium alloy, Procedia CIRP 101 (2021) 226–229 [CrossRef] [Google Scholar]
  33. P. Karmiris-Obratański, N.E. Karkalos, A. Tzotzis, P. Kyratsis, A.P. Markopoulos, Experimental analysis and soft computing modeling of abrasive waterjet milling of steel workpieces, in: MATEC Web Conf., EDP Sciences, 2020, p. 1031 [Google Scholar]
  34. F. Botko, P. Hlaváček, D. Lehocká, V. Foldyna, M. Hatala, V. Simkulet, Effect of abrasive water jet machining on the geometry of shapes in selected tool steels, in: Adv. Water Jet. Sel. Pap. from Int. Conf. Water Jet 2019-Research, Dev. Appl. Novemb. 20-22, 2019, Čeladná, Czech Repub., Springer, 2021, pp. 49–55 [Google Scholar]
  35. R. Singh, V. Singh, T.V.K. Gupta, An experimental study on surface roughness in slicing tungsten carbide with abrasive water jet machining, in: Adv. Mech. Eng. Sel. Proc. ICAME 2020, Springer, 2021, pp. 353–359. [CrossRef] [Google Scholar]
  36. S.P. Jani, A. Senthil Kumar, M.A. Khan, A. Sujin Jose, Design and optimisation of unit production cost for AWJ process on machining hybrid natural fibre composite material, Int. J. Light. Mater. Manuf. 4 (2021) 491–497 [Google Scholar]
  37. F. Ceritbinmez, A. Yapici, An investigation on cutting of the MWCNTs-doped composite plates by AWJ, Arab. J. Sci. Eng. 45 (2020) 5129–5141 [CrossRef] [Google Scholar]
  38. M. Altin Karataş, H. Gökkaya, S. Akincioğlu, M.A. Biberci, Investigation of the effect of AWJ drilling parameters for delamination factor and surface roughness on GFRP composite material, Multidiscip. Model. Mater. Struct. 18 (2022) 734–753 [CrossRef] [Google Scholar]
  39. M. Putz, M. Dix, F. Morczinek, M. Dittrich, Suspension technology for abrasive waterjet (AWJ) cutting of ceramics, Procedia CIRP 77 (2018) 367–370 [CrossRef] [Google Scholar]
  40. P. Wang, X. Miao, M. Wu, P. Zhou, Study on the process of abrasive water jet cutting for zirconia ceramic tubes, Int. J. Adv. Manuf. Technol. (2023) 1–15 [Google Scholar]
  41. D. Mukherjee, W.M. Lim, S. Kumar, N. Donthu, Guidelines for advancing theory and practice through bibliometric research, J. Bus. Res. 148 (2022) 101–115 [CrossRef] [Google Scholar]
  42. N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, W. Marc, How to conduct a bibliometric analysis: An overview and guidelines, J. Bus. Res. 133 (2021) 285–296 [CrossRef] [Google Scholar]
  43. M. Akin, S.P. Eyduran, V. Krauter, Food packaging related research trends in the academic discipline of food science and technology: A bibliometric analysis, Clean. Circ. Bioeconomy 5 (2023) 100046. [CrossRef] [Google Scholar]
  44. M.-H. Wang, Y.-S. Ho, H.-Z. Fu, Global performance and development on sustainable city based on natural science and social science research: A bibliometric analysis, Sci. Total Environ. 666 (2019) 1245–1254 [CrossRef] [Google Scholar]
  45. S. Goyal, S. Chauhan, P. Mishra, Circular economy research: A bibliometric analysis (2000-2019) and future research insights, J. Clean. Prod. 287 (2021) 125011 [CrossRef] [Google Scholar]
  46. J.-A. Marín-Marín, A.-J. Moreno-Guerrero, P. Dúo-Terrón, J. López-Belmonte, STEAM in education: A bibliometric analysis of performance and co-words in Web of Science, Int. J. STEM Educ. 8 (2021) 41 [CrossRef] [Google Scholar]
  47. C. Forliano, P. De Bernardi, D. Yahiaoui, Entrepreneurial universities: A bibliometric analysis within the business and management domains, Technol. Forecast. Soc. Change 165 (2021) 120522 [CrossRef] [Google Scholar]
  48. B.X. Tran, G.T. Vu, G.H. Ha, Q.-H. Vuong, M.-T. Ho, T.-T. Vuong, V.-P. La, M.-T. Ho, K.-C.P. Nghiem, H.L.T. Nguyen, Global evolution of research in artificial intelligence in health and medicine: A bibliometric study, J. Clin. Med. 8 (2019) 360 [CrossRef] [Google Scholar]
  49. M. Bodnariuk, R. Melentiev, Bibliometric analysis of micro-nano manufacturing technologies, Nanotechnol. Precis. Eng. 2 (2019) 61–70 [CrossRef] [Google Scholar]
  50. D. Alexandre, M. Paulo, S. De Arruda, Assessment of researches and case studies on cloud manufacturing: A bibliometric analysis, J. Manuf. Syst. (2021) 691–705 [Google Scholar]
  51. T.C. Dzogbewu, N. Amoah, S.A. Jnr, S.K. Fianko, D.J. de Beer, Multi-material additive manufacturing of electronics components: A bibliometric analysis, Results Eng. 19 (2023) 101318 [CrossRef] [Google Scholar]
  52. M.U. Obi, P. Pradel, M. Sinclair, R. Bibb, A bibliometric analysis of research in design for additive manufacturing, Rapid Prototyp. J. 28 (2022) 967–987 [CrossRef] [Google Scholar]
  53. Y. Bhatt, K. Ghuman, A. Dhir, Sustainable manufacturing: Bibliometrics and content analysis, J. Clean. Prod. 260 (2020) 120988 [CrossRef] [Google Scholar]
  54. R.I. De Oliveira, S.O. Sousa, F.C. De Campos, Lean manufacturing implementation: Bibliometric analysis 2007-2018, Int. J. Adv. Manuf. Technol. 101 (2019) 979–988 [CrossRef] [Google Scholar]
  55. C.-H. Lee, C.-L. Liu, A.J.C. Trappey, J.P.T. Mo, K.C. Desouza, Understanding digital transformation in advanced manufacturing and engineering: A bibliometric analysis, topic modeling and research trend discovery, Adv. Eng. Inform. 50 (2021) 101428 [CrossRef] [Google Scholar]
  56. S. Tiwari, P.C. Bahuguna, R. Srivastava, Smart manufacturing and sustainability: A bibliometric analysis, Benchmarking Int. J. (2022) [Google Scholar]
  57. T. Lv, L. Wang, H. Xie, X. Zhang, Y. Zhang, Evolutionary overview of water resource management (1990-2019) based on a bibliometric analysis in Web of Science, Ecol. Inform. 61 (2021) 101218 [CrossRef] [Google Scholar]
  58. M.J. Becerra, M.A. Pimentel, E.B. De Souza, G.I. Tovar, Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis, Geogr. Sustain. 1 (2020) 209–219 [Google Scholar]
  59. P. García-Sánchez, A.M. Mora, P.A. Castillo, I.J. Pérez, A bibliometric study of the research area of videogames using Dimensions.ai database, Procedia Comput. Sci. 162 (2019) 737–744 [CrossRef] [Google Scholar]
  60. L.M. El Ayoubi, J. El Masri, M. Machaalani, S. El Hage, P. Salameh, Contribution of Arab world in transplant research: A PubMed-based bibliometric analysis, Transpl. Immunol. 68 (2021) 101432 [CrossRef] [Google Scholar]
  61. H.K. Baker, S. Kumar, N. Pandey, Forty years of the Journal of Futures Markets: A bibliometric overview, J. Futur. Mark. 41 (2021) 1027–1054 [CrossRef] [Google Scholar]
  62. Z. Zhen-yu, Z. Qiu-yang, D. Cong, Y. Ju-yu, P. Zhong-yu, A review of the development of surface burnishing process technique based on bibliometric analysis and visualisation, Int. J. Adv. Manuf. Technol. 115 (2021) 1955–1999 [CrossRef] [Google Scholar]
  63. T.C. Dzogbewu, N. Amoah, S.K. Fianko, S. Afrifa, D. De Beer, Additive manufacturing towards product production: A bibliometric analysis, Manuf. Rev. 9 (2022) 1–21 [Google Scholar]
  64. N.J. Griffiths, R.G. Godding, A preliminary investigation into abrasive water jet cutting of cast iron, (1980) [Google Scholar]
  65. J.E. Hirsch, An index to quantify an individual's scientific research output, Proc. Natl. Acad. Sci. USA 102 (2005) 16569–16572 [CrossRef] [Google Scholar]
  66. L. Bertoli-Barsotti, T. Lando, A theoretical model of the relationship between the h-index and other simple citation indicators, Scientometrics 111 (2017) 1415–1448 [CrossRef] [Google Scholar]
  67. M.K. Paliwal, S. Jakhar, V. Sharma, Nano-enhanced phase change materials for energy storage in photovoltaic thermal management systems: A bibliometric and thematic analysis, Int. J. Thermofluids 17 (2023) 100310. [CrossRef] [Google Scholar]
  68. M. Hashish, A modeling study of metal cutting with abrasive waterjets, J. Eng. Mater. Technol. Trans. ASME 106 (1984) 88–100 [CrossRef] [Google Scholar]
  69. T.G. Gutowski, M.S. Branham, J.B. Dahmus, A.J. Jones, A. Thiriez, D.P. Sekulic, Thermodynamic analysis of processes, Environ. Sci. Technol. 43 (2009) 1584–1590 [CrossRef] [Google Scholar]
  70. D. Arola, C.L. Williams, Estimating the fatigue stress concentration factor of machined surfaces, Int. J. Fatigue 24 (2002) 923–930 [CrossRef] [Google Scholar]
  71. D. Herzog, P. Jaeschke, O. Meier, H. Haferkamp, Investigations on the thermal effect caused by laser cutting with respect to static strength of CFRP, Int. J. Mach. Tools Manuf. 48 (2008) 1464–1473 [CrossRef] [Google Scholar]
  72. F. Müller, J. Monaghan, Non-conventional machining of particle reinforced metal matrix composite, Int. J. Mach. Tools Manuf. 40 (2000) 1351–1366 [CrossRef] [Google Scholar]
  73. U. Çaydaş, A. Hasçalık, A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method, J. Mater. Process. Technol. 202 (2008) 574–582 [CrossRef] [Google Scholar]
  74. M. Hashish, A model for abrasive-waterjet (AWJ) machining, J. Eng. Mater. Technol. 111 (1989) 154–162 [CrossRef] [Google Scholar]
  75. D.K. Shanmugam, T. Nguyen, J. Wang, A study of delamination on graphite/epoxy composites in abrasive waterjet machining, Compos. Part A Appl. Sci. Manuf. 39 (2008) 923–92 [CrossRef] [Google Scholar]
  76. M.A. Azmir, A.K. Ahsan, A study of abrasive water jet machining process on glass/epoxy composite laminate, J. Mater. Process. Technol. 209 (2009) 6168–6173 [CrossRef] [Google Scholar]
  77. J. Wang, Abrasive waterjet machining of polymer matrix composites − cutting performance, erosive process and predictive models, Int. J. Adv. Manuf. Technol. 15 (1999) 757–768 [CrossRef] [Google Scholar]
  78. M.A. Azmir, A.K. Ahsan, Investigation on glass/epoxy composite surfaces machined by abrasive water jet machining, J. Mater. Process. Technol. 198 (2008) 122–128 [CrossRef] [Google Scholar]
  79. M.S. ElTobgy, E. Ng, M.A. Elbestawi, Finite element modeling of erosive wear, Int. J. Mach. Tools Manuf. 45 (2005) 1337–1346 [CrossRef] [Google Scholar]
  80. H. Liu, J. Wang, N. Kelson, R.J. Brown, A study of abrasive waterjet characteristics by CFD simulation, J. Mater. Process. Technol. 153–154 (2004) 488–493 [CrossRef] [Google Scholar]
  81. A. Hascalik, U. Çaydaş, H. Gürün, Effect of traverse speed on abrasive waterjet machining of Ti-6Al-4V alloy, Mater. Des. 28 (2007) 1953–1957 [CrossRef] [Google Scholar]
  82. M. Hashish, Pressure effects in abrasive-waterjet (AWJ) machining, J. Eng. Mater. Technol. 111 (1989) 221–228 [CrossRef] [Google Scholar]
  83. D.K. Shanmugam, S.H. Masood, An investigation on kerf characteristics in abrasive waterjet cutting of layered composites, J. Mater. Process. Technol. 209 (2009) 3887–3893 [CrossRef] [Google Scholar]
  84. C. Atas, C. Sevim, On the impact response of sandwich composites with cores of balsa wood and PVC foam, Compos. Struct. 93 (2010) 40–48 [CrossRef] [Google Scholar]
  85. M. Haddad, R. Zitoune, H. Bougherara, F. Eyma, B. Castanié, Study of trimming damages of CFRP structures in function of the machining processes and their impact on the mechanical behaviour, Compos. Part B Eng. 57 (2014) 136–143 [CrossRef] [Google Scholar]
  86. R. Kovacevic, Sensing the abrasive waterjet nozzle wear, Int. J. Waterjet Technol. 2 (1994). [Google Scholar]
  87. N. Yuvaraj, M. Pradeep Kumar, Multiresponse optimisation of abrasive water jet cutting process parameters using TOPSIS approach, Mater. Manuf. Process. 30 (2015) 882–889 [CrossRef] [Google Scholar]
  88. A. Świerczyńska, B. Varbai, C. Pandey, D. Fydrych, Exploring the trends in flux-cored arc welding: Scientometric analysis approach, Int. J. Adv. Manuf. Technol. 126 (2023) 1–24 [CrossRef] [Google Scholar]
  89. H. Sharma, H. Kumar, A. Gupta, M.A. Shah, Computer vision in manufacturing: A bibliometric analysis and future research propositions, Int. J. Adv. Manuf. Technol. 127 (2023) 5691–5710 [CrossRef] [Google Scholar]
  90. P.H. Shipway, G. Fowler, I.R. Pashby, Characteristics of the surface of a titanium alloy following milling with abrasive waterjets, Wear 258 (2005) 123–132 [CrossRef] [Google Scholar]
  91. M. Hashish, Visualisation of the abrasive-waterjet cutting process, Exp. Mech. 28 (1988) 159–169 [CrossRef] [Google Scholar]
  92. M. Hashish, Observations of wear of abrasive-waterjet nozzle materials, J. Tribol. 116 (1994) 439–444) [CrossRef] [Google Scholar]
  93. M. Nanduri, D.G. Taggart, T.J. Kim, C. Haney, F.P. Skeele, Effect of the inlet taper angle on AWJ nozzle wear, in: Proc. 9th Am. Water Jet Conf., 1997, pp. 223–238. [Google Scholar]
  94. A. Akkurt, M.K. Kulekci, U. Seker, F. Ercan, Effect of feed rate on surface roughness in abrasive waterjet cutting applications, J. Mater. Process. Technol. 147 (2004) 389–396 [CrossRef] [Google Scholar]
  95. D. Arola, M. Ramulu, Material removal in abrasive waterjet machining of metals − surface integrity and texture, Wear 210 (1997) 50–58 [CrossRef] [Google Scholar]
  96. G. Aydin, I. Karakurt, K. Aydiner, An investigation on surface roughness of granite machined by abrasive waterjet, Bull. Mater. Sci. 34 (2011) 985–992 [CrossRef] [Google Scholar]
  97. M.K. Babu, O.V.K. Chetty, A study on the use of single mesh size abrasives in abrasive waterjet machining, Int. J. Adv. Manuf. Technol. 29 (2006) 532–540 [CrossRef] [Google Scholar]
  98. L. Chen, Some investigations on AWJ cutting performance, Pergamon 36 (1996) 1201–1206 [Google Scholar]
  99. F.L. Chen, E. Siores, The effect of cutting jet variation on surface striation formation in abrasive water jet cutting, J. Mater. Process. Technol. 135 (2003) 1–5 [CrossRef] [Google Scholar]
  100. F.L. Chen, E. Siores, K. Patel, Improving the cut surface qualities using different controlled nozzle oscillation techniques, Int. J. Mach. Tools Manuf. 42 (2002) 717–722 [CrossRef] [Google Scholar]
  101. M.S. ElTobgy, E. Ng, M.A. Elbestawi, Finite element modeling of erosive wear, Int. J. Mach. Tools Manuf. 45 (2005) 1337–1346 [CrossRef] [Google Scholar]
  102. M. Hashish, On the modeling of abrasive-waterjet cutting, in: Proc. 7th Int. Symp. Jet Cut. Technol., 1984, pp. 249–265 [Google Scholar]
  103. M. Hashish, Observations of wear of abrasive-waterjet nozzle materials, J. Tribol. 116 (1994) 439–444 [CrossRef] [Google Scholar]
  104. M.C. Kong, S. Anwar, J. Billingham, D.A. Axinte, Mathematical modelling of abrasive waterjet footprints for arbitrarily moving jets: Part I—single straight paths, Int. J. Mach. Tools Manuf. 53 (2012) 58–68 [CrossRef] [Google Scholar]
  105. M. Junkar, B. Jurisevic, M. Fajdiga, M. Grah, Finite element analysis of single-particle impact in abrasive water jet machining, Int. J. Impact Eng. 32 (2006) 1095–1112 [CrossRef] [Google Scholar]
  106. H. Liu, J. Wang, N. Kelson, R.J. Brown, A study of abrasive waterjet characteristics by CFD simulation, J. Mater. Process. Technol. 153 (2004) 488–493 [CrossRef] [Google Scholar]
  107. A. Lebar, M. Junkar, Simulation of abrasive water jet cutting process: Part 1. Unit event approach, Model. Simul. Mater. Sci. Eng. 12 (2004) 1159. [CrossRef] [Google Scholar]
  108. V.A. Prabu, S.T. Kumaran, M. Uthayakumar, Performance evaluation of abrasive water jet machining on banana fiber reinforced polyester composite, J. Nat. Fibers 14 (2017) 450–457 [CrossRef] [Google Scholar]
  109. I.W. Ming Ming, A.I. Azmi, L.C. Chuan, A.F. Mansor, Experimental study and empirical analyses of abrasive waterjet machining for hybrid carbon/glass fiber-reinforced composites for improved surface quality, Int. J. Adv. Manuf. Technol. 95 (2018) 3809–3822 [CrossRef] [Google Scholar]
  110. A. Beaucamp, Y. Namba, R. Freeman, Dynamic multiphase modeling and optimisation of fluid jet polishing process, CIRP Ann. 61 (2012) 315–318 [CrossRef] [Google Scholar]
  111. D.H. Ahmed, J. Naser, R.T. Deam, Particles impact characteristics on cutting surface during the abrasive water jet machining: Numerical study, J. Mater. Process. Technol. 232 (2016) 116–130 [CrossRef] [Google Scholar]
  112. C. Narayanan, R. Balz, D.A. Weiss, K.C. Heiniger, Modelling of abrasive particle energy in water jet machining, J. Mater. Process. Technol. 213 (2013) 2201–2210 [CrossRef] [Google Scholar]
  113. U. Prisco, M.C. D'Onofrio, Three-dimensional CFD simulation of two-phase flow inside the abrasive water jet cutting head, Int. J. Comput. Methods Eng. Sci. Mech. 9 (2008) 300–319 [Google Scholar]
  114. P. Lozano Torrubia, Stochastic modelling of abrasive waterjet controlled-depth machining, 2016. [Google Scholar]
  115. A.W. Momber, Energy transfer during the mixing of air and solid particles into a high-speed waterjet: an impact-force study, Exp. Therm. Fluid Sci. 25 (2001) 31–41 [CrossRef] [Google Scholar]
  116. D. Liu, H. Zhu, C. Huang, J. Wang, P. Yao, Prediction model of depth of penetration for alumina ceramics turned by abrasive waterjet—finite element method and experimental study, Int. J. Adv. Manuf. Technol. 87 (2016) 2673–2682 [CrossRef] [Google Scholar]
  117. X. Long, X. Ruan, Q. Liu, Z. Chen, S. Xue, Z. Wu, Numerical investigation on the internal flow and the particle movement in the abrasive waterjet nozzle, Powder Technol. 314 (2017) 635–640 [CrossRef] [Google Scholar]
  118. R. Ruiz-Garcia, P.F. Mayuet Ares, J.M. Vazquez-Martinez, J. Salguero Gómez, Influence of abrasive waterjet parameters on the cutting and drilling of CFRP/UNS A97075 and UNS A97075/CFRP stacks, Materials (Basel) 12 (2018) 107 [CrossRef] [Google Scholar]
  119. A.A. El-Domiaty, A.A. Abdel-Rahman, Fracture mechanics-based model of abrasive waterjet cutting for brittle materials, Int. J. Adv. Manuf. Technol. 13 (1997) 172–181 [CrossRef] [Google Scholar]
  120. M. Haddad, R. Zitoune, H. Bougherara, F. Eyma, B. Castanié, Study of trimming damages of CFRP structures in function of the machining processes and their impact on the mechanical behaviour, Compos. Part B Eng. 57 (2014) 136–143 [CrossRef] [Google Scholar]
  121. A. Hejjaji, R. Zitoune, L. Crouzeix, S. Le Roux, F. Collombet, Surface and machining induced damage characterisation of abrasive water jet milled carbon/epoxy composite specimens and their impact on tensile behaviour, Wear 376 (2017) 1356–1364 [CrossRef] [Google Scholar]
  122. I.M. Hlavacova, V. Geryk, Abrasives for water-jet cutting of high-strength and thick hard materials, Int. J. Adv. Manuf. Technol. 90 (2017) 1217–1224 [CrossRef] [Google Scholar]
  123. B. Jagadeesh, P. Dinesh Babu, M. Nalla Mohamed, P. Marimuthu, Experimental investigation and optimisation of abrasive water jet cutting parameters for the improvement of cut quality in carbon fiber reinforced plastic laminates, J. Ind. Text. 48 (2018) 178–200 [CrossRef] [Google Scholar]
  124. V. Kavimani, P.M. Gopal, K.R. Sumesh, N.V. Kumar, Multi response optimisation on machinability of SiC waste fillers reinforced polymer matrix composite using taguchi's coupled grey relational analysis, Silicon 14 (2022) 65–73 [CrossRef] [Google Scholar]
  125. M. Li, M. Huang, X. Yang, S. Li, K. Wei, Experimental study on hole quality and its impact on tensile behaviour following pure and abrasive waterjet cutting of plain woven CFRP laminates, Int. J. Adv. Manuf. Technol. 99 (2018) 2481–2490 [CrossRef] [Google Scholar]
  126. A. Azhari, C. Schindler, C. Godard, J. Gibmeier, E. Kerscher, Effect of multiple passes treatment in waterjet peening on fatigue performance, Appl. Surf. Sci. 388 (2016) 468–474 [CrossRef] [Google Scholar]
  127. A. Sambruno, F. Bañon, J. Salguero, B. Simonet, M. Batista, Kerf taper defect minimisation based on abrasive waterjet machining of low thickness thermoplastic carbon fiber composites C/TPU, Materials (Basel) 12 (2019) 4192 [CrossRef] [Google Scholar]
  128. D.S. Srinivasu, D.A. Axinte, Mask-less pocket milling of composites by abrasive waterjets: an experimental investigation, J. Manuf. Sci. Eng. 136 (2014) 041005 [CrossRef] [Google Scholar]
  129. K.R. Sumesh, K. Kanthavel, Abrasive water jet machining of Sisal/Pineapple epoxy hybrid composites with the addition of various fly ash filler, Mater. Res. Express 7 (2020) 035303 [CrossRef] [Google Scholar]
  130. R.K. Thakur, K.K. Singh, Experimental investigation and optimisation of abrasive water jet machining parameter on multi-walled carbon nanotube doped epoxy/carbon laminate, Measurement 164 (2020) 108093 [CrossRef] [Google Scholar]
  131. M. Altin Karataş, A.R. Motorcu, H. Gökkaya, Optimisation of machining parameters for kerf angle and roundness error in abrasive water jet drilling of CFRP composites with different fiber orientation angles, J. Brazilian Soc. Mech. Sci. Eng. 42 (2020) 173 [CrossRef] [Google Scholar]
  132. F. Bañon, A. Sambruno, M. Batista, B. Simonet, J. Salguero, Study of the surface quality of carbon fiber-reinforced thermoplastic matrix composite (CFRTP) machined by abrasive water jet (AWJM), Int. J. Adv. Manuf. Technol. 107 (2020) 3299–3313 [CrossRef] [Google Scholar]
  133. A. Dhanawade, S. Kumar, Experimental study of delamination and kerf geometry of carbon epoxy composite machined by abrasive water jet, J. Compos. Mater. 51 (2017) 3373–3390 [CrossRef] [Google Scholar]
  134. M. Armağan, A.A. Arici, Cutting performance of glass-vinyl ester composite by abrasive water jet, Mater. Manuf. Process. 32 (2017) 1715–1722 [CrossRef] [Google Scholar]
  135. P.A. Dumbhare, S. Dubey, Y.V. Deshpande, A.B. Andhare, P.S. Barve, Modelling and multi-objective optimisation of surface roughness and kerf taper angle in abrasive water jet machining of steel, J. Brazilian Soc. Mech. Sci. Eng. 40 (2018) 259 [CrossRef] [Google Scholar]
  136. K. Balaji, M.S. Kumar, N. Yuvaraj, Multi objective Taguchi-grey relational analysis and krill herd algorithm approaches to investigate the parametric optimisation in abrasive water jet drilling of stainless steel, Appl. Soft Comput. 102 (2021) 107075 [CrossRef] [Google Scholar]
  137. A. Nair, S. Kumanan, Multi-performance optimisation of abrasive water jet machining of Inconel 617 using WPCA, Mater. Manuf. Process. 32 (2017) 693–699 [CrossRef] [Google Scholar]
  138. A. Nair, S. Kumanan, Optimisation of size and form characteristics using multi-objective grey analysis in abrasive water jet drilling of Inconel 617, J. Brazilian Soc. Mech. Sci. Eng. 40 (2018) 1–15 [CrossRef] [Google Scholar]
  139. P.J. Pawar, U.S. Vidhate, M.Y. Khalkar, Improving the quality characteristics of abrasive water jet machining of marble material using multi-objective artificial bee colony algorithm, J. Comput. Des. Eng. 5 (2018) 319–328 [Google Scholar]
  140. R.V. Rao, D.P. Rai, J. Balic, Multi-objective optimisation of abrasive waterjet machining process using Jaya algorithm and PROMETHEE method, J. Intell. Manuf. 30 (2019) 2101–2127 [CrossRef] [Google Scholar]
  141. M. Santhanakumar, R. Adalarasan, M. Rajmohan, Experimental modelling and analysis in abrasive waterjet cutting of ceramic tiles using grey-based response surface methodology, Arab. J. Sci. Eng. 40 (2015) 3299–3311 [CrossRef] [Google Scholar]
  142. R. Shukla, D. Singh, Experimentation investigation of abrasive water jet machining parameters using Taguchi and evolutionary optimisation techniques, Swarm Evol. Comput. 32 (2017) 167–183 [CrossRef] [Google Scholar]
  143. R. Shukla, D. Singh, Selection of parameters for advanced machining processes using firefly algorithm, Eng. Sci. Technol. an Int. J. 20 (2017) 212–221 [CrossRef] [Google Scholar]
  144. N. Yusup, A. Sarkheyli, A.M. Zain, S.Z.M. Hashim, N. Ithnin, Estimation of optimal machining control parameters using artificial bee colony, J. Intell. Manuf. 25 (2014) 1463–1472 [CrossRef] [Google Scholar]
  145. A.M. Zain, H. Haron, S. Sharif, Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA, Expert Syst. Appl. 38 (2011) 8316–8326 [CrossRef] [Google Scholar]
  146. A.M. Zain, H. Haron, S. Sharif, Optimisation of process parameters in the abrasive waterjet machining using integrated SA-GA, Appl. Soft Comput. 11 (2011) 5350–5359 [CrossRef] [Google Scholar]
  147. S. Chakraborty, A. Mitra, Parametric optimisation of abrasive water-jet machining processes using grey wolf optimizer, Mater. Manuf. Process. 33 (2018) 1471–1482 [CrossRef] [Google Scholar]
  148. A.M. Zain, H. Haron, S. Sharif, Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining, Eng. Comput. 27 (2011) 251–259 [CrossRef] [Google Scholar]
  149. S. Chakraborty, P.P. Das, V. Kumar, Application of grey-fuzzy logic technique for parametric optimisation of non-traditional machining processes, Grey Syst. Theory Appl. 8 (2018) 46–68 [CrossRef] [Google Scholar]
  150. Jagadish, S. Bhowmik, A. Ray, Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach, J. Intell. Manuf. 30 (2019) 2965–2979 [CrossRef] [Google Scholar]
  151. A. Khan, K.P. Maity, Application of MCDM-based TOPSIS method for the optimisation of multi quality characteristics of modern manufacturing processes, Int. J. Eng. Res. Africa. 23 (2016) 33–51 [CrossRef] [Google Scholar]
  152. A. Khan, K.P. Maity, Parametric optimisation of some non-conventional machining processes using MOORA method, Int. J. Eng. Res. Africa. 20 (2016) 19–40 [Google Scholar]
  153. M. Manoj, G.R. Jinu, T. Muthuramalingam, Multi response optimisation of AWJM process parameters on machining TiB2 particles reinforced Al7075 composite using Taguchi-DEAR methodology, Silicon 10 (2018) 2287–2293 [CrossRef] [Google Scholar]
  154. A. Mat Deris, A. Mohd Zain, R. Sallehuddin, Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining, Meccanica 48 (2013) 1937–1945 [CrossRef] [Google Scholar]
  155. M.A. Mellal, E.J. Williams, Parameter optimisation of advanced machining processes using cuckoo optimisation algorithm and hoopoe heuristic, J. Intell. Manuf. 27 (2016) 927–942 [CrossRef] [Google Scholar]
  156. D. Arola, M.L. McCain, S. Kunaporn, M. Ramulu, Waterjet and abrasive waterjet surface treatment of titanium: a comparison of surface texture and residual stress, Wear 249 (2001) 943–950 [CrossRef] [Google Scholar]
  157. D. Arola, C.L. Williams, Estimating the fatigue stress concentration factor of machined surfaces, Int. J. Fatigue 24 (2002) 923–930 [CrossRef] [Google Scholar]
  158. D. Arola, A.E. Alade, W. Weber, Improving fatigue strength of metals using abrasive waterjet peening, Mach. Sci. Technol. 10 (2006) 197–218 [CrossRef] [Google Scholar]
  159. J. Holmberg, J. Berglund, A. Wretland, T. Beno, Evaluation of surface integrity after high energy machining with EDM, laser beam machining and abrasive water jet machining of alloy 718, Int. J. Adv. Manuf. Technol. 100 (2019) 1575–1591 [CrossRef] [Google Scholar]
  160. M.C. Kong, D. Axinte, Response of titanium aluminide alloy to abrasive waterjet cutting: geometrical accuracy and surface integrity issues versus process parameters, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 223 (2009) 19–42 [CrossRef] [Google Scholar]
  161. K.B. Mardi, A.R. Dixit, A. Mallick, A. Pramanik, B. Ballokova, P. Hvizdos, J. Foldyna, J. Scucka, P. Hlavacek, M. Zelenak, Surface integrity of Mg-based nanocomposite produced by abrasive water jet machining (AWJM), Mater. Manuf. Process. 32 (2017) 1707–1714 [CrossRef] [Google Scholar]
  162. A.K. Srivastava, A. Nag, A.R. Dixit, S. Tiwari, J. Scucka, M. Zelenak, S. Hloch, P. Hlavacek, Surface integrity in tangential turning of hybrid MMC A359/B4C/Al2O3 by abrasive waterjet, J. Manuf. Process. 28 (2017) 11–20 [CrossRef] [Google Scholar]
  163. I. Karakurt, G. Aydin, K. Aydiner, Effect of the abrasive grain size on the cutting performance of concrete in AWJ technology, Technology 13 (2010) 145–150 [Google Scholar]
  164. G. Aydin, Performance of recycling abrasives in rock cutting by abrasive water jet, J. Cent. South Univ. 22 (2015) 1055–1061 [CrossRef] [Google Scholar]
  165. G. Aydin, I. Karakurt, K. Aydiner, Performance of abrasive waterjet in granite cutting: influence of the textural properties, J. Mater. Civ. Eng. 24 (2012) 944–949 [CrossRef] [Google Scholar]
  166. G. Aydin, S. Kaya, I. Karakurt, Utilisation of solid-cutting waste of granite as an alternative abrasive in abrasive waterjet cutting of marble, J. Clean. Prod. 159 (2017) 241–247 [CrossRef] [Google Scholar]
  167. I. Karakurt, G. Aydin, K. Aydiner, Analysis of the kerf angle of the granite machined by abrasive waterjet (AWJ), (2011) [Google Scholar]
  168. S. Liu, Y. Cui, Y. Chen, C. Guo, Numerical research on rock breaking by abrasive water jet-pick under confining pressure, Int. J. Rock Mech. Min. Sci. 120 (2019) 41–49 [CrossRef] [Google Scholar]
  169. S. Liu, F. Zhou, H. Li, Y. Chen, F. Wang, C. Guo, Experimental investigation of hard rock breaking using a conical pick assisted by abrasive water jet, Rock Mech. Rock Eng. 53 (2020) 4221–4230 [CrossRef] [Google Scholar]
  170. Y. Lu, J. Tang, Z. Ge, B. Xia, Y. Liu, Hard rock drilling technique with abrasive water jet assistance, Int. J. Rock Mech. Min. Sci. 60 (2013) 47–56 [CrossRef] [Google Scholar]
  171. A.I. Hassan, C. Chen, R. Kovacevic, On-line monitoring of depth of cut in AWJ cutting, Int. J. Mach. Tools Manuf. 44 (2004) 595–605 [CrossRef] [Google Scholar]
  172. S. Hloch, J. Valíček, Prediction of distribution relationship of titanium surface topography created by abrasive waterjet, Int. J. Surf. Sci. Eng. 5 (2011) 152–168 [CrossRef] [Google Scholar]
  173. S. Hloch, J. Valíček, Topographical anomaly on surfaces created by abrasive waterjet, Int. J. Adv. Manuf. Technol. 59 (2012) 593–604 [CrossRef] [Google Scholar]
  174. P. Hreha, A. Radvanska, L. Knapcikova, G.M. Królczyk, S. Legutko, J.B. Królczyk, S. Hloch, P. Monka, Roughness parameters calculation by means of on-line vibration monitoring emerging from AWJ interaction with material, Metrol. Meas. Syst. 22 (2015) 315–326 [CrossRef] [Google Scholar]
  175. R. Pahuja, M. Ramulu, Surface quality monitoring in abrasive water jet machining of Ti6Al4V-CFRP stacks through wavelet packet analysis of acoustic emission signals, Int. J. Adv. Manuf. Technol. 104 (2019) 4091–4104 [CrossRef] [Google Scholar]
  176. R. Kovacevic, H.S. Kwak, R.S. Mohan, Acoustic emission sensing as a tool for understanding the mechanisms of abrasive water jet drilling of difficult-to-machine materials, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 212 (1998) 45–58 [CrossRef] [Google Scholar]
  177. V. Peržel, P. Hreha, S. Hloch, H. Tozan, J. Valíček, Vibration emission as a potential source of information for abrasive waterjet quality process control, Int. J. Adv. Manuf. Technol. 61 (2012) 285–294 [CrossRef] [Google Scholar]
  178. A. Rabani, I. Marinescu, D. Axinte, Acoustic emission energy transfer rate: A method for monitoring abrasive waterjet milling, Int. J. Mach. Tools Manuf. 61 (2012) 80–89 [CrossRef] [Google Scholar]
  179. A. Alberdi, A. Rivero, L.N. López De Lacalle, I. Etxeberria, A. Suárez, Effect of process parameter on the kerf geometry in abrasive water jet milling, Int. J. Adv. Manuf. Technol. 51 (2010) 467–480 [CrossRef] [Google Scholar]
  180. G. Fowler, I.R. Pashby, P.H. Shipway, The effect of particle hardness and shape when abrasive water jet milling titanium alloy Ti6Al4V, Wear 266 (2009) 613–620 [CrossRef] [Google Scholar]
  181. M. Hashish, Controlled-depth milling of isogrid structures with AWJs, (1998). [Google Scholar]
  182. A. Alberdi, A. Rivero, L.N. López de Lacalle, Experimental study of the slot overlapping and tool path variation effect in abrasive waterjet milling, (2011). [Google Scholar]
  183. P.K. Farayibi, J.W. Murray, L. Huang, F. Boud, P.K. Kinnell, A.T. Clare, Erosion resistance of laser clad Ti-6Al-4V/WC composite for waterjet tooling, J. Mater. Process. Technol. 214 (2014) 710–72 [CrossRef] [Google Scholar]
  184. Z. Zhang, T. Yu, R. Kovacevic, Erosion and corrosion resistance of laser cladded AISI 420 stainless steel reinforced with VC, Appl. Surf. Sci. 410 (2017) 225–240 [CrossRef] [Google Scholar]
  185. M. Mieszala, P.L. Torrubia, D.A. Axinte, J.J. Schwiedrzik, Y. Guo, S. Mischler, J. Michler, L. Philippe, Erosion mechanisms during abrasive waterjet machining: model microstructures and single particle experiments, J. Mater. Process. Technol. 247 (2017) 92–102 [CrossRef] [Google Scholar]
  186. J. Wang, Particle velocity models for ultra-high pressure abrasive waterjets, J. Mater. Process. Technol. 209 (2009) 4573–4577 [CrossRef] [Google Scholar]

Cite this article as: Nuraini Lusi, I Gusti Ngurah Bagus Catrawedarma, Mebrahitom Gebremariam, Kushendarsyah Saptaji, Azmir Azhari, A four-decade of abrasive waterjet processing technology (1980-2023): a scientometric analysis, Manufacturing Rev. 12, 15 (2025), https://doi.org/10.1051/mfreview/2025011

All Tables

Table 1

The top 9 groups for the subject areas of the study (data from Scopus database).

In the text
Table 2

The datasets collected.

In the text
Table 3

Research publication growth.

In the text
Table 4

The top 20 highly cited journals within the subject of AWJ research.

In the text
Table 5

The author's local Impact in AWJ research.

In the text
Table 6

lists the most-cited articles.

In the text
Table 7

List of documents Co-citation articles in the clusters (1980-2023).

In the text
Table 8

Proposed areas for future investigation cited in publications with significant influence.

In the text

All Figures

thumbnail Fig. 1

The document category obtained from the Scopus database.
In the text
thumbnail Fig. 2

A bibliometric review's methodology.
In the text
thumbnail Fig. 3

Annual scientific production.
In the text
thumbnail Fig. 4

The average yearly amount of acquired citations.
In the text
thumbnail Fig. 5

The 20 most productive countries of AWJ research.
In the text
thumbnail Fig. 6

The Global perspective on the research productivity of nations from 1980 to 2023.
In the text
thumbnail Fig. 7

The international collaboration's map between countries.
In the text
thumbnail Fig. 8

Network diagram of author keywords (threshold: 10 co-occurrence) and normalisation method (Association strength).
In the text
thumbnail Fig. 9

Network diagram for index keywords (threshold: 20 co-occurrence).
In the text
thumbnail Fig. 10

Word cloud of keyword plus.
In the text
thumbnail Fig. 11

Bibliographic coupling of articles.
In the text

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